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AI Compliance & Governance Services
Secure your AI infrastructure. Indext Data Lab provides end-to-end AI compliance, algorithmic auditing, and governance frameworks. We validate model interpretability, data privacy, and algorithmic fairness while strictly adhering to regulatory standards like the EU AI Act, GDPR, and the NIST AI Risk Management Framework.
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
The Core Engineering Challenge
Deploying machine learning models in production introduces non-deterministic risks that traditional software compliance cannot catch. You need more than a checklist; you need a technical architecture that enforces safety.
At Indext Data Lab, we treat governance as code. We move beyond theory and build compliance directly into your MLOps (Machine Learning Operations) pipeline. This ensures your models remain robust against drift, bias, and adversarial attacks from the training phase through to inference.
Technical Stack & Instrumentation
We use a hardened stack of open-source and proprietary tools to test, validate, and monitor your AI systems. We integrate these tools directly into your existing CI/CD (Continuous Integration/Continuous Deployment) workflows
Algorithmic Auditing & Fairness
We test for disparate impact and bias across protected groups
- IBM AI Fairness 360 (AIF360): We use this to detect and fix bias in datasets and models.
- Fairlearn: We apply this for group fairness metrics assessment during model selection.
- What-If Tool (WIT): We use this for probing model behavior across different hypothetical situations.
SHAP (SHapley Additive exPlanations): We calculate the contribution of each feature to the prediction.
LIME (Local Interpretable Model-agnostic Explanations): We use this to approximate the model locally and explain individual predictions.
LIME (Local Interpretable Model-agnostic Explanations): We use this to approximate the model locally and explain individual predictions.
- ELI5: We deploy this to debug machine learning classifiers and check their inference steps.
Explainability & Interpretability (XAI)
We make "black box" models transparent so stakeholders understand why a decision was made
Data Privacy & Security
We secure the data lineage (the lifecycle of data origin and movement) and prevent leakage
- TensorFlow Privacy: We apply differential privacy (adding noise to obscure individual data points) to train models without exposing user data.
- PySyft: We use this for encrypted, privacy-preserving deep learning.
- CleverHans: We test your models against adversarial examples (inputs designed to trick the model) to ensure robustness.
Why Governance Matters
Reduce Regulatory Latency
Global standards are changing fast. The EU AI Act and GDPR impose heavy fines for non-compliance. We build adaptable frameworks that let you pivot quickly when laws change. This prevents operational downtime and keeps your legal team happy.
Prevent Model Drift & Decay
Models degrade over time. "Drift" happens when the live data changes and no longer matches the training data. Our governance protocols include automated drift detection. This alerts your engineers to retrain models before they lose accuracy and hurt revenue.
Build Trust with Stakeholders
Users trust systems they understand. By providing explainability artifacts (reports showing how models think), you reduce friction with customers and internal auditors. This speeds up adoption and lowers the risk of reputational damage from "biased AI" headlines.
Our Execution Workflow
We use a four-phase process to audit, fix, and maintain your AI infrastructure.
Discovery & Taxonomy
We map your entire AI surface area.
Inventory: We list all deployed models, APIs, and shadow AI (unauthorized tools).
Risk Categorization: We rank systems based on the NIST AI RMF tiers (Low, Medium, High Risk).
Data Lineage Mapping: We trace where your training data comes from and check if you have the rights to use it.
Inventory: We list all deployed models, APIs, and shadow AI (unauthorized tools).
Risk Categorization: We rank systems based on the NIST AI RMF tiers (Low, Medium, High Risk).
Data Lineage Mapping: We trace where your training data comes from and check if you have the rights to use it.
Stress Test
We try to break your models.
Bias Testing: We run your models against synthetic datasets to check for discrimination.
Adversarial Attack Simulation: We inject noise and edge cases to see if the model fails.
Code Review: We analyze your Jupyter notebooks and training scripts for reproducibility and security flaws.
Bias Testing: We run your models against synthetic datasets to check for discrimination.
Adversarial Attack Simulation: We inject noise and edge cases to see if the model fails.
Code Review: We analyze your Jupyter notebooks and training scripts for reproducibility and security flaws.
Remediation & Hardening
We fix the broken parts.
Model Retraining: We re-weight datasets or apply in-processing algorithms to remove bias.
Documentation: We generate Model Cards (standardized documents detailing model limits) for every asset.
Access Control: We set up RBAC (Role-Based Access Control) to limit who can deploy or modify models.
Model Retraining: We re-weight datasets or apply in-processing algorithms to remove bias.
Documentation: We generate Model Cards (standardized documents detailing model limits) for every asset.
Access Control: We set up RBAC (Role-Based Access Control) to limit who can deploy or modify models.
Continuous Monitoring
We set up the watchtower.
Alerting Systems: We configure Prometheus and Grafana to track model performance metrics in real-time.
Human-in-the-Loop (HITL): We build workflows where low-confidence predictions are sent to a human for review.
Audit Trails: We log every prediction and version change for future legal defense.
Alerting Systems: We configure Prometheus and Grafana to track model performance metrics in real-time.
Human-in-the-Loop (HITL): We build workflows where low-confidence predictions are sent to a human for review.
Audit Trails: We log every prediction and version change for future legal defense.
FAQ: Common Questions
AI Safety focuses on preventing unintended harm or accidents caused by the model (e.g., a self-driving car crashing). AI Compliance focuses on adhering to specific laws and regulations (e.g., ensuring the car's data collection follows privacy laws). We handle both to ensure total system integrity.
If your US company processes data from EU citizens or sells AI services in the EU, you must follow the Act. It uses an extraterritorial scope. We help you map your systems to the EU's four risk categories to ensure you are ready for these cross-border rules.
Yes. While we cannot always see the internal math of a deep neural network, we use post-hoc explainability tools like SHAP and LIME. These tools analyze the inputs and outputs to reverse-engineer why the model made a specific decision, satisfying most regulatory "right to explanation" rules.
You should audit continuously. One-time audits fail because data changes. We recommend setting up automated monitoring that runs daily, with a deep manual audit performed quarterly or whenever you push a major version update to production.
No. We are technical compliance engineers. We work alongside your legal counsel. We provide the technical evidence, logs, and reports that your lawyers need to prove you are following the law. We bridge the gap between code and the courtroom.
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